Perceptual Asymmetry Between Hue Categories: Evidence from Human Color Categorization

2026-05-10

AI Summary

This study challenges the common assumption in computational models that color categories are uniformly distributed in perceptual space, demonstrating instead their pronounced non-uniformity based on large-scale human color-naming data. Building upon the COLIBRI fuzzy color model, the work introduces two novel metrics—Wideness and Boundary Width—to quantify, respectively, the extent of hue category coverage and the uncertainty at category boundaries. Through geometric analysis at the α = 0.5 membership level, the research reveals for the first time a systematic asymmetry in color category organization: yellow exhibits a compact spatial extent with sharp boundaries, whereas green spans a broad region with extended, gradual transitions. These findings underscore that color naming is not only inherently fuzzy but also significantly non-uniform, thereby refuting traditional uniformity assumptions in color categorization models.

Abstract

Human color categories are not uniformly distributed in perceptual space, yet most computational color models still assume fixed and evenly structured representations. In this paper, we present a focused analytical extension of the COLIBRI fuzzy color model by investigating perceptual asymmetry between hue categories. Using previously collected large-scale human color categorization data, we introduce quantitative measures of category extent and boundary uncertainty, namely Wideness and Boundary Width, derived from fuzzy membership functions at the α = 0.5 level. The analysis reveals a strong imbalance between the two categories: yellow occupies a compact and sharply constrained region of the hue space, whereas green spans a substantially broader interval and exhibits a more extended transition structure. The results show that perceptual color categories are not only fuzzy, but also highly non-uniform in their geometric organization. This asymmetry suggests that some categories behave as narrow, highly specific perceptual labels, while others function as broad, tolerant regions of human color naming. These findings provide a new perspective on linguistic color categorization and extend the interpretability of the COLIBRI framework for perceptually grounded color modeling.

Problem

• perceptual asymmetry
• color categorization
• hue categories
• fuzzy color model
• perceptual space

Innovation

• perceptual asymmetry
• fuzzy color modeling
• color categorization
• COLIBRI
• boundary uncertainty